Embracing Variability in Psychiatric Disorders: Insights from Normative Models of Brain Morphometry

Over the past decades, a major goal in biological psychiatry has been to identify brain biomarkers of psychiatric disorders. However, numerous barriers, including significant clinical and biological heterogeneity, have hindered the progress toward identifying clinically relevant neuroimaging markers and translating neuroimaging findings into clinical practice. Traditional approaches often overlook this variability, treating clinical cohorts as homogeneous. In this talk, I will introduce normative modeling of brain morphometry, a statistically rigorous framework for making inferences at the individual level. Unlike traditional group-level analyses, normative modelling captures variability by estimating a normative distribution for a given phenotype (e.g., brain volume) based on relevant demographic characteristics (e.g., age, sex). Individual-level deviations from these norms can then be measured to better understand clinical and biological variability without assuming shared patterns of pathology or predefined clusters. Over the past decade, various research groups have developed several tools and frameworks to obtain normative models of brain morphometry, such as the PCNToolkit, CentileBrain, and BrainCharts. I will outline the intuition behind these normative modeling approaches/methods, emphasizing its potential to advance our understanding of the neurobiology of psychiatric disorders and improving treatment outcomes. I will then discuss some of the most recent applications across a variety of clinical samples (neurodegenerative, neurodevelopmental, and psychiatric disorders) in different clinical contexts (treatment outcomes, illness stage, transdiagnostic samples) using various neuroimaging correlates (gray matter volume, cortical thickness, fMRI, surface area). I will conclude by illustrating how normative modelling encourages us to reframe variability as a feature rather than a limitation in psychiatric neuroimaging research by presenting recent work combining normative models of brain morphology with genetics to more precisely identify aspects of brain dysfunction that may have casual influences on psychiatric phenotypes, and demonstrating how multiscale approaches might be useful to improve our understanding of the neurobiology of psychiatric disorders.